plotInteractionEffect {mlegp} | R Documentation |
Displays a contour plot that shows the 2-way interaction effect of two parameters from a Gaussian process
plotInteractionEffect(gp, effects, length1.out = 21, length2.out = 21, lower = apply(gp$X, 2, min), upper = apply(gp$X, 2, max), no.plot = FALSE)
gp |
an object of class gp |
effects |
a vector containing 2 elements corresponding to the parameter numbers or names to plot |
length1.out |
the number of values to consider for the first parameter |
length2.out |
the number of values to consider for the second parameter |
lower |
a vector of minimum values for ALL parameters of the gp design matrix |
upper |
a vector of maximum values for ALL parameters of the gp design matrix |
no.plot |
set no.plot to TRUE to turn plotting off and to return the interaction effects |
An interaction effect of two parameters, x and y, is the predicted output when x and y are fixed, and the remaining parameters are integrated out according to a prior distribution that is independent U(a,b) for all parameters, where for each parameter, (a,b) corresponds to the (min,max) of that parameter in the design. These values can also be overwritten by the arguments lower
and upper
.
This function produces a contour plot of predictions for all combinations of the parameters in effects
, where the values of the first parameter are
seq(lower[effects[1]], upper[effects[1]], length.out = length1.out) and values for the second parameter are
seq(lower[effects[2]], upper[effects[2]], length.out = length2.out).
For elements not in effects
, lower
and upper
will determine the limits of integration.
If no.plot
is TRUE
, a list with components:
index1 |
vector of parameter values for the first parameter |
index2 |
vector of parameter values for the second parameter |
preds |
a matrix containing the predicted values to be plotted by contour |
Garrett M. Dancik dancikg@nsula.edu
Schonlau, M. and Welch, W. 2006. Screening the Input Variables to a Computer Model Via Analysis of Variance and Visualization, in Screening: Methods for Experimentation in Industry, Drug Discovery, and Genetics. A Dean and S. Lewis, eds. (New York: Springer).
http://users.nsula.edu/dancikg/mlegp/
FANOVADecomposition
for quantifying the importance of an interaction effect
## fit the Gaussian process ## x1 = kronecker(seq(0,1,by=.25), rep(1,5)) x2 = rep(seq(0,1,by=.25),5) z = 4 * x1 - 2*x2 + x1 * x2 + rnorm(length(x1), sd = 0.001) fit = mlegp(cbind(x1,x2), z, param.names = c("x1", "x2")) ## plot the interaction effect ## plotInteractionEffect(fit, effects = c(1,2)) ## plot the interaction effect 'manually' ## int = plotInteractionEffect(fit, effects = c(1,2), no.plot = TRUE) contour(int$index1, int$index2, int$preds, xlab = "x1", ylab = "x2")